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    Numerical Investigation of Traffic State Reconstruction and Control Using Connected Automated Vehicles

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    Type
    Preprint
    Authors
    Cicic, Mladen
    Barreau, Matthieu
    Johansson, Karl Henrik
    KAUST Grant Number
    OSR-2019-CRG8-4033
    Date
    2020-10-06
    Permanent link to this record
    http://hdl.handle.net/10754/665562
    
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    Abstract
    In this paper we present a numerical study on control and observation of traffic flow using Lagrangian measurements and actuators. We investigate the effect of some basic control and observation schemes using probe and actuated vehicles within the flow. The aim is to show the effect of the state reconstruction on the efficiency of the control, compared to the case using full information about the traffic. The effectiveness of the proposed state reconstruction and control algorithms is demonstrated in simulations. They show that control using the reconstructed state approaches the full-information control when the gap between the connected vehicles is not too large, reducing the delay by more than $60\%$ when the gap between the sensor vehicles is $1.25$~km on average, compared to a delay reduction of almost $80\%$ in the full-information control case. %It shows that the reconstruction is well achieved if the distance between two connected and autonomous vehicles is not too large (more than 1 vehicle per kilometer). %We also show that it is possible to further improve the control performance by employing a simple adaptive probe vehicle selection scheme. Moreover, we propose a simple scheme for selecting which vehicles to use as sensors, in order to reduce the communication burden. Numerical simulations demonstrate that %It also demonstrates that it is possible to reduce the communication burden using a triggering mechanism with with this triggering mechanism, the delay is reduced by around $65\%$, compared to a reduction of $72\%$ if all connected vehicles are communicating at all times.
    Citation
    ITSC 2020, Rhodes, Greece
    Sponsors
    The research leading to these results has received funding from the KAUST Office of Sponsored Research under Award No. OSR-2019-CRG8-4033, the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 674875, VINNOVA within the FFI program under contract 2014-06200, the Swedish Research Council, the Swedish Foundation for Strategic Research and Knut and Alice Wallenberg Foundation. The authors are affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP).
    Publisher
    arXiv
    DOI
    10.1109/itsc45102.2020.9294351
    arXiv
    2010.02564
    Additional Links
    https://arxiv.org/pdf/2010.02564
    ae974a485f413a2113503eed53cd6c53
    10.1109/itsc45102.2020.9294351
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